Efficient Random Assignment under a Combination of Ordinal and Cardinal Information on Preferences

Consider a collection of m indivisible objects to be allocated to n agents, where m = n. Each agent falls in one of two distinct categories: either he (a) has a complete ordinal ranking over the set of individual objects, or (b) has a set of “plausible” benchmark von Neumann-Morgenstern (vNM) utility functions in whose non-negative span his “true” utility is known to lie. An allocation is undominated if there does not exist a preference-compatible profile of vNM utilities at which it is Pareto dominated by another feasible allocation. Given an undominated allocation, we use the tools of linear duality theory to construct a profile of vNM utilities at which it is ex-ante welfare maximizing. A finite set of preference-compatible vNM utility profiles is exhibited such that every undominated allocation is ex-ante welfare maximizing with respect to at least one of them. Given an arbitrary allocation, we provide an interpretation of the constructed vNM utilities as subgradients of a function which measures worst-case domination.Random Assignment, Efficiency, Duality, Linear Programming

Consistent estimation of regression models with incompletely observed exogenous variables

Economics

CP-nets: A Tool for Representing and Reasoning withConditional Ceteris Paribus Preference Statements

Information about user preferences plays a key role in automated decision making. In many domains it is desirable to assess such preferences in a qualitative rather than quantitative way. In this paper, we propose a qualitative graphical representation of preferences that reflects conditional dependence and independence of preference statements under a ceteris paribus (all else being equal) interpretation. Such a representation is often compact and arguably quite natural in many circumstances. We provide a formal semantics for this model, and describe how the structure of the network can be exploited in several inference tasks, such as determining whether one outcome dominates (is preferred to) another, ordering a set outcomes according to the preference relation, and constructing the best outcome subject to available evidence

Pigouvian pricing and stochastic evolutionary implementation

pricing;game theory

When Are Preferences Consistent? The Effects of Task Familiarity and Contextual Cues on Revealed and Stated Preferences

Traditionally, economists make a sharp distinction between stated and revealed preferences, viewing the latter as more fully meeting the assumptions of economic analysis. Here, we consider one form of empirical evidence regarding this belief: the consistency of choices in stated and revealed preference tasks. We show that both kinds of task can produce consistent choices, suggesting that both can measure underlying preferences, if necessary conditions are met. We propose that a necessary condition is that task be either familiar to those facing it or offer contextual cues that substitute for familiarity, such as prices in competitive markets or recommendations from trusted, knowledgeable sources. We show that how well decision makers achieve such understanding is often confounded with the method that researchers use. Considering task familiarity not only clarifies some of the conflicting evidence regarding revealed and stated preference methods, but raises potentially productive questions regarding the roles of social institutions in shaping preferences.Consistency, contingent valuation, framing, public goods, revealed preferences, stated preferences, validity